{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']     # 显示中文\n",
    "# 为了坐标轴负号正常显示。matplotlib默认不支持中文，设置中文字体后，负号会显示异常。需要手动将坐标轴负号设为False才能正常显示负号。\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "   Unnamed: 0      UserID  EI  NS  TF  JP MBTI类型  粉丝数  关注数  性别  ...  贴  感到  \\\n0           0  1330417035   1   1   1   0   ISFJ   71  276   0  ...  0   0   \n\n   时刻  一份  吃  回复  个人  喜欢  快来  干净  \n0   0   0  0   0   0   0   1   0  \n\n[1 rows x 2002 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>UserID</th>\n      <th>EI</th>\n      <th>NS</th>\n      <th>TF</th>\n      <th>JP</th>\n      <th>MBTI类型</th>\n      <th>粉丝数</th>\n      <th>关注数</th>\n      <th>性别</th>\n      <th>...</th>\n      <th>贴</th>\n      <th>感到</th>\n      <th>时刻</th>\n      <th>一份</th>\n      <th>吃</th>\n      <th>回复</th>\n      <th>个人</th>\n      <th>喜欢</th>\n      <th>快来</th>\n      <th>干净</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1330417035</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>ISFJ</td>\n      <td>71</td>\n      <td>276</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1 rows × 2002 columns</p>\n</div>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = pd.read_csv('../data/mbti_weibo_data_cantrain1500.csv')\n",
    "all_data.head(1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "       UserID  EI  NS  TF  JP MBTI类型\n0  1330417035   1   1   1   0   ISFJ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>UserID</th>\n      <th>EI</th>\n      <th>NS</th>\n      <th>TF</th>\n      <th>JP</th>\n      <th>MBTI类型</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1330417035</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>ISFJ</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_data = all_data.iloc[:,1:7]\n",
    "target_data.head(1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "   粉丝数  关注数  性别  微博数  注册年限  互动数  视频累积播放量  TOP1  TOP2  TOP3  ...  贴  感到  时刻  \\\n0   71  276   0  655    11  109        0    29    20    14  ...  0   0   0   \n\n   一份  吃  回复  个人  喜欢  快来  干净  \n0   0  0   0   0   0   1   0  \n\n[1 rows x 1995 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>粉丝数</th>\n      <th>关注数</th>\n      <th>性别</th>\n      <th>微博数</th>\n      <th>注册年限</th>\n      <th>互动数</th>\n      <th>视频累积播放量</th>\n      <th>TOP1</th>\n      <th>TOP2</th>\n      <th>TOP3</th>\n      <th>...</th>\n      <th>贴</th>\n      <th>感到</th>\n      <th>时刻</th>\n      <th>一份</th>\n      <th>吃</th>\n      <th>回复</th>\n      <th>个人</th>\n      <th>喜欢</th>\n      <th>快来</th>\n      <th>干净</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>71</td>\n      <td>276</td>\n      <td>0</td>\n      <td>655</td>\n      <td>11</td>\n      <td>109</td>\n      <td>0</td>\n      <td>29</td>\n      <td>20</td>\n      <td>14</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1 rows × 1995 columns</p>\n</div>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_data0 = all_data.iloc[:,7:2002]\n",
    "features_data0.head(1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "Z = 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    "]]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1197 entries, 0 to 1196\n",
      "Columns: 1884 entries, 粉丝数 to 干净\n",
      "dtypes: int64(1884)\n",
      "memory usage: 17.2 MB\n"
     ]
    }
   ],
   "source": [
    "Z.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from sklearn.feature_selection import (SelectKBest, chi2, SelectPercentile, SelectFromModel, SequentialFeatureSelector, SequentialFeatureSelector)\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "y_EI = target_data[\"EI\"]\n",
    "y_NS = target_data[\"NS\"]\n",
    "y_TF = target_data[\"TF\"]\n",
    "y_JP = target_data[\"JP\"]\n",
    "\n",
    "y_list = {\"y_EI\":y_EI,\"y_NS\":y_NS,\"y_TF\":y_TF,\"y_JP\":y_JP}\n",
    "X  = features_data0"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "def trainModelTest(X,y):\n",
    "    X  = scaler.fit_transform(X)  #对自变量X做标准化处理\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "\n",
    "    # 创建逻辑回归模型\n",
    "    log = LogisticRegression()\n",
    "    log.fit(X_train, y_train)\n",
    "    # score_l = log.score(X_test,y_test)\n",
    "\n",
    "     # 预测测试集结果\n",
    "    y_pred0 = log.predict(X_train)\n",
    "\n",
    "    y_pred = log.predict(X_test)\n",
    "    score_l = log.score(X_test,y_test)\n",
    "    result ={'accuracy':score_l,'X_train':X_train, 'X_test':X_test, 'y_train':y_train, 'y_test':y_test,'y_pred':y_pred ,'y_pred0':y_pred0,'model':log}\n",
    "\n",
    "    return result"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "#神经网络\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "#做标准化处理(神经网络会对数值敏感，数值越大它会认为越重要，因此需要标准差)\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "# input_features = scaler.fit_transform(features)  #对自变量X做标准化处理\n",
    "\n",
    "def trainFCNN(X_train, X_test, y_train, y_test):\n",
    "\n",
    "    #标签\n",
    "    labels = np.array(y_train)\n",
    "    #特征\n",
    "    input_features = X_train\n",
    "    #特征名列表\n",
    "    # feature_list = list(features.columns)\n",
    "\n",
    "    #定义网络\n",
    "    input_size = input_features.shape[1]\n",
    "    hidden_size = 24\n",
    "    hidden_size2 = 48\n",
    "    hidden_size3 = 128\n",
    "    hidden_size4 = 256\n",
    "    hidden_size5 = 512\n",
    "    output_size = 1\n",
    "    batch_size = 1#分批训练\n",
    "\n",
    "    my_nn = torch.nn.Sequential(\n",
    "        torch.nn.Linear(input_size,hidden_size3),\n",
    "        torch.nn.ReLU(),\n",
    "        torch.nn.Linear(hidden_size3,hidden_size4),\n",
    "        torch.nn.ReLU(),\n",
    "        torch.nn.Linear(hidden_size4,output_size)\n",
    "    )\n",
    "\n",
    "    cost = torch.nn.MSELoss(reduction='mean')\n",
    "    optimizer = torch.optim.Adam(my_nn.parameters(),lr=0.001)\n",
    "\n",
    "    #训练网络\n",
    "    losses = []\n",
    "    for i in range(10):\n",
    "        batch_loss = []\n",
    "        #MINI-Batch方法来训练\n",
    "        for start in range(0,len(input_features),batch_size):\n",
    "            end = start + batch_size if start +batch_size < len(input_features) else len(input_features)\n",
    "            xx = torch.tensor(input_features[start:end], dtype= torch.float, requires_grad= True)\n",
    "            yy = torch.tensor(labels[start:end], dtype= torch.float, requires_grad= True)\n",
    "            prediction = my_nn(xx)\n",
    "            loss = cost(prediction,yy)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward(retain_graph=True)\n",
    "            optimizer.step()\n",
    "            batch_loss.append(loss.data.numpy())\n",
    "\n",
    "        #打印损失\n",
    "        if i%10==0:\n",
    "            losses.append(np.mean(batch_loss))\n",
    "            # print(i,np.mean(batch_loss))\n",
    "\n",
    "    #预测训练结果\n",
    "    x0 = torch.tensor(X_train,dtype=torch.float)\n",
    "    y_pred0 = my_nn(x0).data.numpy()\n",
    "    y_pred0 = [1 if i > 0.5 else 0 for i in y_pred0]  # 将预测值转化为类别标签\n",
    "\n",
    "    x = torch.tensor(X_test,dtype=torch.float)\n",
    "    y_pred = my_nn(x).data.numpy()\n",
    "    y_pred = [1 if i > 0.5 else 0 for i in y_pred]  # 将预测值转化为类别标签\n",
    "\n",
    "    # 计算准确率\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    result ={'accuracy':accuracy,'X_train':X_train, 'X_test':X_test, 'y_train':y_train, 'y_test':y_test,'y_pred':y_pred ,'y_pred0':y_pred0,'model':my_nn}\n",
    "    # print(accuracy)\n",
    "    return result"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_EI\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "500\n",
      "600\n",
      "700\n",
      "y_NS\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "500\n",
      "600\n",
      "700\n",
      "y_TF\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "500\n",
      "600\n",
      "700\n",
      "y_JP\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "500\n",
      "600\n",
      "700\n"
     ]
    }
   ],
   "source": [
    "bestlog= []\n",
    "allResult = {}\n",
    "y_name =  [\"y_EI\",\"y_NS\",\"y_TF\",'y_JP']\n",
    "# y_name = [\"y_NS\"]\n",
    "# y_name = [\"y_TF\"]\n",
    "# y_name = [\"y_JP\"]\n",
    "\n",
    "for name in y_name:\n",
    "    print(name)\n",
    "    y = y_list[name]\n",
    "    bestlog_Score = 0\n",
    "    bestlog_new_features = \"\"\n",
    "    bestlog_Logresult=[]\n",
    "\n",
    "    for k in range(5,800):\n",
    "        if k%100==0:\n",
    "            print(k)\n",
    "\n",
    "        # selector = SelectKBest(score_func=f_regression, k=k)\n",
    "        selector = SelectKBest(score_func=chi2, k=k)\n",
    "        X_new = selector.fit_transform(X, y)\n",
    "\n",
    "        # 输出结果\n",
    "        mask = selector.get_support() # 获得特征掩码\n",
    "        new_features = X.columns[mask] # 选择重要的特征\n",
    "\n",
    "\n",
    "        Logresult = trainModelTest(X[new_features],y)\n",
    "        logScore = Logresult['accuracy']\n",
    "\n",
    "        if logScore >bestlog_Score:\n",
    "                bestlog_Score = logScore\n",
    "                bestlog_new_features=new_features\n",
    "                bestlog_Logresult=Logresult\n",
    "    allResult['LOG_'+name]=bestlog_Logresult\n",
    "    allResult['LOG_'+name+'_features']=bestlog_new_features\n",
    "    allResult['FCNN_'+name]=trainFCNN(bestlog_Logresult['X_train'],bestlog_Logresult['X_test'],bestlog_Logresult['y_train'],bestlog_Logresult['y_test'])\n",
    "\n",
    "    bestlog.append({\"name\":name,\"k\":k,\"bestlog_Score\":bestlog_Score,\"bestlog_new_features\":bestlog_new_features})\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOG_y_EI  :  0.7291666666666666\n",
      "FCNN_y_EI  :  0.6583333333333333\n",
      "LOG_y_NS  :  0.7333333333333333\n",
      "FCNN_y_NS  :  0.6625\n",
      "LOG_y_TF  :  0.7333333333333333\n",
      "FCNN_y_TF  :  0.6625\n",
      "LOG_y_JP  :  0.7583333333333333\n",
      "FCNN_y_JP  :  0.6833333333333333\n"
     ]
    }
   ],
   "source": [
    "allResult2 = allResult\n",
    "for record in allResult2:\n",
    "    if len(allResult2[record])==8:\n",
    "        print(record,\" : \",allResult2[record]['accuracy'])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6083333333333333\n",
      "0.5\n",
      "0.5625\n",
      "0.5791666666666667\n"
     ]
    }
   ],
   "source": [
    "y_name =  [\"y_EI\",\"y_NS\",\"y_TF\",'y_JP']\n",
    "# y_name = [\"y_NS\"]\n",
    "# y_name = [\"y_TF\"]\n",
    "# y_name = [\"y_JP\"]\n",
    "oldmodels_pre_list =[]\n",
    "oldmodels_y_list =[]\n",
    "for name in y_name:\n",
    "    y = y_list[name]\n",
    "    Z  = scaler.fit_transform(Z)  #对自变量X做标准化处理\n",
    "    X_train, X_test, y_train, y_test = train_test_split(Z, y, test_size=0.2, random_state=1)\n",
    "\n",
    "    # 创建逻辑回归模型\n",
    "    log = LogisticRegression()\n",
    "    log.fit(X_train, y_train)\n",
    "    score_l = log.score(X_test,y_test)\n",
    "    print(score_l)\n",
    "     # 预测测试集结果\n",
    "\n",
    "    y_pred = log.predict(X_test)\n",
    "    oldmodels_pre_list.append(y_pred)\n",
    "    oldmodels_y_list.append(y_test)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "#画图\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "def printRoc(type,y_test,y_pred_lg,y_pred_fcnn,y_test_old,y_pred_old):\n",
    "    fpr_logistic = roc_curve(y_test, y_pred_lg)[0]\n",
    "    tpr_logistic = roc_curve(y_test, y_pred_lg)[1]\n",
    "    auc_logistic = auc(fpr_logistic, tpr_logistic)\n",
    "\n",
    "    fpr_fcnn = roc_curve(y_test, y_pred_fcnn)[0]\n",
    "    tpr_fcnn = roc_curve(y_test, y_pred_fcnn)[1]\n",
    "    auc_fcnn = auc(fpr_fcnn, tpr_fcnn)\n",
    "\n",
    "    fpr_old = roc_curve(y_test_old, y_pred_old)[0]\n",
    "    tpr_old = roc_curve(y_test_old, y_pred_old)[1]\n",
    "    auc_old = auc(fpr_old, tpr_old)\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.lineplot(x=fpr_logistic, y=tpr_logistic, label='Logistic (AUC = {:.2f})'.format(auc_logistic))\n",
    "    sns.lineplot(x=fpr_fcnn, y=tpr_fcnn, label='FCNN (AUC = {:.2f})'.format(auc_fcnn))\n",
    "    sns.lineplot(x=fpr_old, y=tpr_old, label='Before Logistic (AUC = {:.2f})'.format(auc_old))\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC Curves of '+type)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x700 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x700 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x700 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x700 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "# allResult2 = allResult\n",
    "listroc = [['LOG_y_EI','FCNN_y_EI'],['LOG_y_NS','FCNN_y_NS'],['LOG_y_TF','FCNN_y_TF'],['LOG_y_JP','FCNN_y_EI']]\n",
    "names =  [\"EI\",\"NS\",\"TF\",'JP']\n",
    "# for record in allResult2:\n",
    "#     if len(allResult2[record])==8:\n",
    "#         print(record,\" : \",allResult2[record]['accuracy'])\n",
    "\n",
    "for i in range(len(names)):\n",
    "        logmsg =  allResult2[listroc[i][0]]\n",
    "        fcnnmsg =  allResult2[listroc[i][1]]\n",
    "        y_test = logmsg['y_test']\n",
    "        y_pred_lg = logmsg['y_pred']\n",
    "        y_pred_fcnn = fcnnmsg['y_pred']\n",
    "        name = names[i]\n",
    "\n",
    "        printRoc(name,y_test,y_pred_lg,y_pred_fcnn,oldmodels_y_list[i],oldmodels_pre_list[i])\n",
    "\n",
    "        # 计算混淆矩阵\n",
    "        cm = confusion_matrix(y_test, y_pred_lg)\n",
    "        \n",
    "        # 使用 Seaborn 的 heatmap 函数画混淆矩阵\n",
    "        plt.figure(figsize=(10,7))\n",
    "        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "        plt.xlabel('Predicted')\n",
    "        plt.ylabel('True')\n",
    "        plt.title('Logistic-'+name)\n",
    "        plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "[{'name': 'y_EI',\n  'k': 799,\n  'bestlog_Score': 0.7416666666666667,\n  'bestlog_new_features': Index(['粉丝数', '关注数', '微博数', '注册年限', '互动数', '视频累积播放量', 'TOP1', 'TOP2', 'TOP3',\n         'TOP4',\n         ...\n         '美的', '吃饭', '好吃', '幸福', '拍', '海', '爸', '咖啡', '头衔', '快来'],\n        dtype='object', length=110)},\n {'name': 'y_NS',\n  'k': 799,\n  'bestlog_Score': 0.7375,\n  'bestlog_new_features': Index(['粉丝数', '关注数', '微博数', '注册年限', '互动数', '视频累积播放量', 'TOP1', 'TOP3', 'TOP5',\n         '历史_x',\n         ...\n         '一声', '到底', '恭喜', '不行', '韩剧', '就是说', '像是', '根本', '周五', '感到'],\n        dtype='object', length=151)},\n {'name': 'y_TF',\n  'k': 799,\n  'bestlog_Score': 0.7041666666666667,\n  'bestlog_new_features': Index(['粉丝数', '关注数', '微博数', '注册年限', '互动数', '视频累积播放量', 'TOP1', 'TOP2', 'TOP4',\n         '科学科普',\n         ...\n         '说话', '面前', '见面', '初心', '相册', '共情', '惹', '闹钟', '周六', '不让'],\n        dtype='object', length=344)},\n {'name': 'y_JP',\n  'k': 799,\n  'bestlog_Score': 0.7458333333333333,\n  'bestlog_new_features': Index(['粉丝数', '关注数', '性别', '微博数', '注册年限', '互动数', '视频累积播放量', 'TOP1', 'TOP2',\n         'TOP3',\n         ...\n         '闹钟', '根本', '明明', '这段', '收', '本来', '好开心', '每天', '时刻', '干净'],\n        dtype='object', length=595)}]"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestlog"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "def bestLGBM(X_train,y_train,X_test,y_test):\n",
    "    param_grid = {\n",
    "    'n_estimators': [200, 500, 800],\n",
    "    'learning_rate': [0.01, 0.05, 0.1],\n",
    "    'max_depth': [3, 5, 7],\n",
    "    'num_leaves': [31, 50, 100]\n",
    "    }\n",
    "\n",
    "    lgbm = LGBMClassifier(random_state=42)\n",
    "    grid_search = GridSearchCV(lgbm, param_grid, cv=5, scoring='accuracy')\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    print('最佳参数组合：', grid_search.best_params_)\n",
    "    print('最佳得分：', grid_search.best_score_)\n",
    "    best_lgbm = grid_search.best_estimator_\n",
    "    y_pred = best_lgbm.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_EI\n",
      "0.5791666666666667\n",
      "['粉丝数', '关注数', '性别', '微博数', '互动数', '视频累积播放量', 'TOP1', 'TOP3', 'TOP4', 'TOP5', '母婴育儿', '科学科普', '教育', '军事', '汽车', '宗教', 'readability1', 'readability2', 'readability3', '乐_num', '好_num', '恶_num', 'stopword_num', 'word_num', 'all_num', 'y', 'uv', 'uj', 'df', 'tg', 'j', 'd', 'an', 'ag', 'ul', 'h', 'rz', 'nr', 'k', 'q', 'v', 'o', 'nz', 'vg', 'e', 'vq', 'nt', 'p', 'n', 'nrt', 'ug', 'c', 'r', 'ns', 'yg', '依旧', '痛', '回来', '勿忘', '辛苦', '头疼', '年龄', '怀念', '精神', '当下', '愿望', '码', '视角', '记录', '仙品', '要是', '历史_y', '少年', '睡不着', '丢', '结束', '人格', '承认', '旅行', '必要', '还好', '初雪', '存在', '至少', '师姐', '帮助', '电视剧_y', '二传', '一步', '手机', '女朋友', '难过', '聊', '倒霉', '不愧', '下来', '收到', '删了', '煮', '微光', '碎', '任何人', '姐姐', '我服', '天天开心', '破防', '物料', '助力', '是因为', '不要', '早就', '专辑', '前行', '官方', '无聊', '各位', '梦见', '每次', '小姐姐', '健康', '半夜', '辣', '自由', '碰到', '上学', '搜', '恋爱', '解释', '节目', '安心', '分析', '抖音', '草莓', '大哥', '赢', '头发', '游戏_y', '莲花', '楼', '唱', '舞台', '想起', '医院', '小猫', '漂亮', '生日快乐', '使用', '珍惜', '估计', '酸奶', '下辈子', '测试', '最大', '肯定', '要来', '要么', '尊嘟', '教室', '生病', '好不容易', '不好意思', '逼', '小宝', '块钱', '迟到', '这么久', '两次', '亲爱', '对象', '好奇', '姨妈', '铭记', '眼神', '影视', '螺蛳', '太会', '正在', '饿', '算是', '机带', '遗憾', '挂', '专属', '嘎嘎', '闪耀', '参与', '六级', '宝贝', '没钱', '失去', '链接', '没用', '问问', '天杀', '然后', '作文', '腿', '紧张', '意识', '心疼', '情绪', '雨', '兄弟', '领导', '未来', '傻', '最最', '答案', '茶', '下雨', '对方', '我点', '短暂', '总是', '嗑', '我爸', '一路', '自强', '梦想', '生日', '最后', '欲', '学医', '而已', '顺利', '再也', '震惊', '欣赏', '性格', '好美', '近期', '哎呀', '放', '战队', '海', '演唱会', '消失', '聊天', '垃圾', '神经病', '转', '那天', '追', '皮肤', '不断', '热爱', '一场', '贱', '办公室', '小朋友', '乖乖', '讨厌', '吾辈', '中秋', '不得不', '昨天晚上', '舍不得', '对不起', '不在', '共同', '感叹', '国家', '关于', '摸鱼', '震撼', '见面', '初心', '不行', '嗨', '太帅', '李', '闹钟', '明明', '收', '本来', '时刻']\n",
      "y_NS\n",
      "0.5291666666666667\n",
      "['粉丝数', '关注数', '性别', '微博数', '注册年限', '互动数', '视频累积播放量', 'TOP1', 'TOP2', 'TOP3', 'TOP4', 'TOP5', '母婴育儿', '互联网', '体育', '科学科普', '教育', '军事', '汽车', '宗教', '摄影拍照', '旅游出行', 'readability1', 'readability2', 'readability3', '乐_num', '好_num', '哀_num', '恶_num', 'stopword_num', 'word_num', 'all_num', 'nrfg', 'y', 'uv', 'uj', 'df', 'tg', 'ud', 'j', 'd', 'an', 'ag', 'ul', 'h', 'rz', 'nr', 'k', 'q', 'v', 'ng', 'o', 'nz', 'a', 'vg', 'e', 'vq', 'nt', 'p', 'n', 'nrt', 'ug', 'c', 'r', 'ns', 'yg', '经历', '其实', '依旧', '空调', '新歌', '休息', '红包', '更好', '有没有', '痛', '试试', '回来', '勿忘', '现实', '只会', '辛苦', '头疼', '应该', '注意', '崽', '年龄', '怀念', '精神', '当下', '正确', '愿望', '最爱', '来说', '真实', '码', '不够', '路人', '下班', '纠结', '搭', '视角', '付出', '记录', '月亮', '仙品', '抽', '要是', '历史_y', '满足', '时代', '太阳', '少年', '获得', '英语', '一切顺利', '关系', '直播', '任务', '睡不着', '丢', '结束', '论文', '人格', '同学', '承认', '晚安', '旅行', '图片', '表现', '嘞', '早点', '必要', '当时', '还好', '听到', '初雪', '存在', '至少', '台历', '逻辑', '抽奖', '师姐', '拼', '帮助', '认为', '对着', '聊聊吧', '瘦', '电视剧_y', '哪个', '十二月', '他人', '再次', '二传', '一生', '一步', '手机', '该死', '女朋友', '打算', '刚', '难过', '聊', '倒霉', '哥哥', '不愧', '没看', '下来', '收到', '删了', '忘', '感动', '煮', '微光', '碎', '任何人', '坚定', '送', '蝴蝶', '姐姐', '女生', '我服', '天天开心', '破防', '小心', '内耗', '角色', '物料', '助力', '今晚', '羡慕', '成为', '礼物', '是因为', '不要', '春天', '早就', '特典', '风景', '没错', '一次', '梦到', '专辑', '帽子', '前行', '官方', '绽放', '文案', '地球', '感谢', '无聊', '各位', '只能', '站', '碎片', '一眼', '梦见', '美', '好烦', '每次', '置顶', '小姐姐', '健康', '为啥', '半夜', '辣', '自由', '碰到', '奔赴', '只要', '清醒', '这辈子', '心中', '压力', '如果', '一套', '等到', '上学', '黄朔', '少', '大部分', '男人', '搜', '上线', '抖', '来自', '起床', '高铁', '恋爱', '解释', '一把', '思考', '节目', '抽中', '暖', '安心', '分析', '抖音', '好久没', '听歌', '养', '草莓', '大哥', '互相', '卧槽', '赢', '外卖', '勇敢', '头发', '游戏_y', '视频', '莲花', '小时', '一般', '北京', '楼', '任何', '好困', '一切', '唱', '舞台', '想起', '医院', '小猫', '家庭', '漂亮', '脸上', '私信', '生日快乐', '内娱', '使用', '努力', '遇见', '苹果', '跨', '反而', '珍惜', '微信', '跨年', '发疯', '图书馆', '估计', '最佳', '认真', '老公', '一条', '灵魂', '酸奶', '懒得', '超话', '越', '豆', '阿姨', '下辈子', '意义', '安慰', '有意思', '好笑', '不对', '二改', '测试', '幸运', '支持', '最大', '中国', '肯定', '稳定', '秋天', '类型', '熬夜', '过年', '要来', '跳舞', '表达', '干什么', '要么', '小号', '尊嘟', '教室', '生病', '好不容易', '不好意思', '谢谢', '快递', '逼', '小宝', '块钱', '三点', '迟到', '这么久', '事情', '两次', '女性', '亲爱', '折磨', '相机', '对象', '绝了', '如何', '好奇', '忘记', '年纪', '特种兵', '姨妈', '原谅', '演员', '铭记', '早', '梗', '眼神', '影视', '状态', '青春', '不住', '螺蛳', '太会', '好消息', '正在', '呦', '之前', '饿', '算是', '机带', '遗憾', '爱意', '挂', '诶', '专属', '出去', '期末', '嘎嘎', '实力', '闪耀', '参与', '六级', '宝贝', '没钱', '失去', '团', '在意', '链接', '没用', '问问', '天杀', '容易', '我们', '然后', '后悔', '作文', '买到', '自拍', '分享', '腿', '紧张', '笔记', '降温', '白鹿', '意识', '尴尬', '声音', '星座', '心疼', '满意', '胖', '第一个', '情绪', '成绩', '抱抱', '雨', '心态', '兄弟', '送出', '超爱', '结局', '领导', '委屈', '加', '好像', '未来', '美丽', '真心', '傻', '系列', '心动', '最最', '期待', '爸爸妈妈', '答案', '男生', '茶', '下雨', '竟然', '年轻', '对方', '我点', '不如', '面包', '短暂', '总是', '描绘', '困', '社会', '嗑', '我爸', '学校', '号', '一路', '自强', '包', '梦想', '生日', '最后', '欲', '棒', '粉丝', '嘿嘿嘿', '学医', '而已', '顺利', '再也', '十年', '很少', '震惊', '立马', '直接', '欣赏', '几个', '性格', '炸', '好难', '好美', '近期', '哎呀', '放', '命运', '免费', '战队', '海', '演唱会', '提前', '消失', '聊天', '垃圾', '好玩', '热', '爸', '救命', '神经病', '转', '寒假', '晒太阳', '那天', '难道', '照顾', '今年', '食堂', '追', '皮肤', '饱饱', '卷', '不断', '热爱', '一场', '一顿', '贱', '办公室', '搞笑', '小朋友', '汪苏', '全部', '怪', '乖乖', '路', '讨厌', '吾辈', '中秋', '不得不', '昨天晚上', '舍不得', '没想到', '对不起', '水果', '就要', '无语', '不在', '共同', '回应', '可能', '考研', '感叹', '导师', '国家', '觉得', '关于', '鸡', '优秀', '摸鱼', '一遍', '烦', '上午', '说话', '震撼', '暑假', '愿', '见面', '啃', '精神状态', '好萌', '初心', '相册', '不行', '韩剧', '看着', '只是', '摆烂', '蹲', '嗨', '太帅', '会员', '李', '头衔', '可惜', '想法', '闹钟', '根本', '明明', '这段', '收', '本来', '好开心', '每天', '时刻', '干净']\n",
      "y_TF\n",
      "0.5625\n",
      "['粉丝数', '关注数', '性别', '微博数', '注册年限', '互动数', '视频累积播放量', 'TOP1', 'TOP2', 'TOP3', 'TOP4', 'TOP5', '母婴育儿', '互联网', '体育', '科学科普', '教育', '军事', '汽车', '宗教', '摄影拍照', 'readability1', 'readability2', 'readability3', '乐_num', '好_num', '哀_num', '恶_num', 'stopword_num', 'word_num', 'all_num', 'nrfg', 'y', 'uv', 'uj', 'df', 'tg', 'ud', 'j', 'd', 'an', 'ag', 'ul', 'h', 'rz', 'nr', 'k', 'q', 'v', 'ng', 'o', 'nz', 'a', 'vg', 'e', 'vq', 'nt', 'p', 'n', 'nrt', 'ug', 'c', 'r', 'ns', 'yg', '经历', '其实', '依旧', '空调', '红包', '痛', '试试', '回来', '勿忘', '现实', '只会', '辛苦', '头疼', '应该', '注意', '年龄', '怀念', '精神', '当下', '正确', '愿望', '最爱', '来说', '真实', '码', '路人', '下班', '纠结', '搭', '视角', '付出', '记录', '仙品', '要是', '历史_y', '满足', '时代', '太阳', '少年', '英语', '一切顺利', '关系', '直播', '睡不着', '丢', '结束', '论文', '人格', '同学', '承认', '旅行', '图片', '表现', '早点', '必要', '当时', '还好', '听到', '初雪', '存在', '至少', '台历', '逻辑', '抽奖', '师姐', '拼', '帮助', '认为', '对着', '聊聊吧', '瘦', '电视剧_y', '十二月', '再次', '二传', '一生', '一步', '手机', '该死', '女朋友', '打算', '难过', '聊', '倒霉', '哥哥', '不愧', '下来', '收到', '删了', '忘', '感动', '煮', '微光', '碎', '任何人', '坚定', '送', '姐姐', '女生', '我服', '天天开心', '破防', '小心', '内耗', '角色', '物料', '助力', '今晚', '羡慕', '成为', '是因为', '不要', '春天', '早就', '特典', '风景', '没错', '梦到', '专辑', '帽子', '前行', '官方', '文案', '感谢', '无聊', '各位', '只能', '站', '碎片', '一眼', '梦见', '美', '好烦', '每次', '置顶', '小姐姐', '健康', '为啥', '半夜', '辣', '自由', '碰到', '奔赴', '只要', '清醒', '这辈子', '心中', '压力', '如果', '一套', '等到', '上学', '黄朔', '少', '大部分', '搜', '上线', '抖', '来自', '起床', '恋爱', '解释', '一把', '思考', '节目', '抽中', '暖', '安心', '分析', '抖音', '好久没', '听歌', '养', '草莓', '大哥', '互相', '卧槽', '赢', '勇敢', '头发', '游戏_y', '莲花', '小时', '一般', '北京', '楼', '任何', '好困', '一切', '唱', '舞台', '想起', '医院', '小猫', '家庭', '漂亮', '私信', '生日快乐', '内娱', '使用', '努力', '遇见', '苹果', '跨', '反而', '珍惜', '微信', '发疯', '估计', '最佳', '认真', '一条', '灵魂', '酸奶', '懒得', '超话', '阿姨', '下辈子', '安慰', '有意思', '好笑', '不对', '二改', '测试', '幸运', '支持', '最大', '中国', '肯定', '秋天', '熬夜', '过年', '要来', '跳舞', '表达', '干什么', '要么', '小号', '尊嘟', '教室', '生病', '好不容易', '不好意思', '逼', '小宝', '块钱', '迟到', '这么久', '事情', '两次', '亲爱', '折磨', '相机', '对象', '绝了', '如何', '好奇', '忘记', '年纪', '特种兵', '姨妈', '演员', '铭记', '早', '眼神', '影视', '状态', '不住', '螺蛳', '太会', '正在', '呦', '饿', '算是', '机带', '遗憾', '爱意', '挂', '诶', '专属', '期末', '嘎嘎', '实力', '闪耀', '参与', '六级', '宝贝', '没钱', '失去', '团', '在意', '链接', '没用', '问问', '天杀', '容易', '我们', '然后', '后悔', '作文', '买到', '分享', '腿', '紧张', '降温', '白鹿', '意识', '尴尬', '声音', '星座', '心疼', '满意', '胖', '第一个', '情绪', '成绩', '抱抱', '雨', '心态', '兄弟', '送出', '超爱', '结局', '领导', '委屈', '加', '好像', '未来', '真心', '傻', '系列', '最最', '爸爸妈妈', '答案', '男生', '茶', '下雨', '竟然', '年轻', '对方', '我点', '不如', '短暂', '总是', '描绘', '困', '社会', '嗑', '我爸', '学校', '号', '一路', '自强', '梦想', '生日', '最后', '欲', '棒', '粉丝', '学医', '而已', '顺利', '再也', '十年', '很少', '震惊', '立马', '直接', '欣赏', '几个', '性格', '好难', '好美', '近期', '哎呀', '放', '命运', '免费', '战队', '海', '演唱会', '提前', '消失', '聊天', '垃圾', '好玩', '热', '神经病', '转', '寒假', '晒太阳', '那天', '照顾', '今年', '追', '皮肤', '饱饱', '卷', '不断', '热爱', '一场', '一顿', '贱', '办公室', '搞笑', '小朋友', '汪苏', '怪', '乖乖', '路', '讨厌', '吾辈', '中秋', '不得不', '昨天晚上', '舍不得', '没想到', '对不起', '水果', '无语', '不在', '共同', '回应', '可能', '感叹', '导师', '国家', '关于', '鸡', '优秀', '摸鱼', '一遍', '烦', '上午', '说话', '震撼', '暑假', '愿', '见面', '啃', '精神状态', '好萌', '初心', '相册', '不行', '韩剧', '看着', '只是', '蹲', '嗨', '太帅', '会员', '李', '头衔', '可惜', '闹钟', '根本', '明明', '这段', '收', '本来', '每天', '时刻', '干净']\n",
      "y_JP\n",
      "0.5791666666666667\n",
      "['粉丝数', '关注数', '性别', '微博数', '互动数', '视频累积播放量', 'TOP1', 'TOP3', 'TOP4', 'TOP5', '母婴育儿', '科学科普', '教育', '军事', '汽车', '宗教', '摄影拍照', 'readability1', 'readability2', 'readability3', '乐_num', '好_num', '恶_num', 'stopword_num', 'word_num', 'all_num', 'nrfg', 'y', 'uv', 'uj', 'df', 'tg', 'j', 'd', 'an', 'ag', 'ul', 'h', 'rz', 'nr', 'k', 'q', 'v', 'ng', 'o', 'nz', 'a', 'vg', 'e', 'vq', 'nt', 'p', 'n', 'nrt', 'ug', 'c', 'r', 'ns', 'yg', '其实', '依旧', '痛', '回来', '勿忘', '辛苦', '头疼', '年龄', '怀念', '精神', '当下', '愿望', '最爱', '真实', '码', '路人', '视角', '付出', '记录', '仙品', '要是', '历史_y', '时代', '少年', '一切顺利', '睡不着', '丢', '结束', '论文', '人格', '同学', '承认', '旅行', '图片', '必要', '还好', '听到', '初雪', '存在', '至少', '抽奖', '师姐', '帮助', '认为', '聊聊吧', '瘦', '电视剧_y', '再次', '二传', '一步', '手机', '女朋友', '难过', '聊', '倒霉', '不愧', '下来', '收到', '删了', '感动', '煮', '微光', '碎', '任何人', '姐姐', '女生', '我服', '天天开心', '破防', '小心', '内耗', '物料', '助力', '羡慕', '是因为', '不要', '早就', '专辑', '前行', '官方', '无聊', '各位', '碎片', '梦见', '好烦', '每次', '小姐姐', '健康', '为啥', '半夜', '辣', '自由', '碰到', '清醒', '压力', '上学', '黄朔', '搜', '上线', '抖', '起床', '恋爱', '解释', '思考', '节目', '抽中', '安心', '分析', '抖音', '草莓', '大哥', '互相', '赢', '勇敢', '头发', '游戏_y', '莲花', '楼', '任何', '一切', '唱', '舞台', '想起', '医院', '小猫', '漂亮', '私信', '生日快乐', '内娱', '使用', '苹果', '珍惜', '微信', '估计', '灵魂', '酸奶', '懒得', '下辈子', '测试', '最大', '中国', '肯定', '要来', '跳舞', '表达', '要么', '尊嘟', '教室', '生病', '好不容易', '不好意思', '逼', '小宝', '块钱', '迟到', '这么久', '事情', '两次', '亲爱', '对象', '好奇', '忘记', '年纪', '姨妈', '铭记', '眼神', '影视', '螺蛳', '太会', '正在', '呦', '饿', '算是', '机带', '遗憾', '爱意', '挂', '专属', '期末', '嘎嘎', '闪耀', '参与', '六级', '宝贝', '没钱', '失去', '在意', '链接', '没用', '问问', '天杀', '然后', '作文', '腿', '紧张', '白鹿', '意识', '星座', '心疼', '满意', '情绪', '成绩', '雨', '心态', '兄弟', '送出', '领导', '加', '未来', '傻', '最最', '答案', '男生', '茶', '下雨', '对方', '我点', '短暂', '总是', '社会', '嗑', '我爸', '号', '一路', '自强', '梦想', '生日', '最后', '欲', '棒', '学医', '而已', '顺利', '再也', '很少', '震惊', '直接', '欣赏', '性格', '好美', '近期', '哎呀', '放', '战队', '海', '演唱会', '提前', '消失', '聊天', '垃圾', '神经病', '转', '那天', '照顾', '追', '皮肤', '卷', '不断', '热爱', '一场', '一顿', '贱', '办公室', '搞笑', '小朋友', '汪苏', '乖乖', '路', '讨厌', '吾辈', '中秋', '不得不', '昨天晚上', '舍不得', '对不起', '水果', '不在', '共同', '可能', '感叹', '国家', '关于', '鸡', '摸鱼', '烦', '上午', '说话', '震撼', '愿', '见面', '好萌', '初心', '不行', '只是', '嗨', '太帅', '李', '闹钟', '明明', '这段', '收', '本来', '时刻', '干净']\n"
     ]
    }
   ],
   "source": [
    "bestlog= []\n",
    "y_name = [\"y_EI\",\"y_NS\",\"y_TF\",\"y_JP\"]\n",
    "bestk_list = [276,594,529,363]\n",
    "\n",
    "for i in range(len(y_name)):\n",
    "    print(y_name[i])\n",
    "    k = bestk_list[i]\n",
    "    y = y_list[name]\n",
    "    bestlog_Score = 0\n",
    "    bestlog_new_features = \"\"\n",
    "\n",
    "    selector = SelectKBest(score_func=chi2, k=k)\n",
    "    X_new = selector.fit_transform(X, y)\n",
    "\n",
    "     # 输出结果\n",
    "    mask = selector.get_support() # 获得特征掩码\n",
    "    new_features = X.columns[mask] # 选择重要的特征\n",
    "\n",
    "\n",
    "    logScore = lgbTrain(X[new_features],y)\n",
    "    print(logScore)\n",
    "    print(list(new_features))\n",
    "    bestlog.append({\"name\":name,\"k\":k,\"bestLGB_Score\":bestlog_Score,\"bestLGBnew_features\":list(new_features)})\n",
    "\n",
    "\n",
    "pd.DataFrame(bestlog).to_csv('../result/lgbres_t2.csv')"
   ],
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